Map-Reduce Word Count
This project is implemented in Python using gRPC. The input files are given in .txt format and the word count operation is performed.
The medium article for understansing the code better is Learn gRPC with an Example
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program’s execution across a set of machines, handling machine failures, and managing the required inter-machine communication
Installation and Usage
Clone this repository:
$ git clone https://github.com/divija-swetha/coding-exercise.git
$ python -V Python 3.8.5 $ python -m pip install grpcio $ python -m pip install grpcio tools
There are three main files called the client, worker and driver. The client gives the input files and the number of output files and the worker ports (e.g. 127.0.0.1:4001). The worker nodes are launched with their ports and are responsible for the map and reduce operations. The driver takes the input from the client and distributes the work among all the worker nodes.
The code implenation begins with writing the proto files for the driver and worker.
Driver file launches the data processing operation, which is carried out when
rpc launchDriver (launchData) returns (status); is executed in the code.
Once the proto file is written, the following command is executed in the terminal.
$ python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. driver.proto
This generates two files in the directory named, driver_pb2_grpc.py and driver_pb2.py.
The worker file sets the driver port, carries out the map and reduce operations. An additional method, die, is provided to terminate the process. The worker methods are as follows:
rpc setDriverPort(driverPort) returns (status); rpc map(mapInput) returns (status); rpc reduce (rid) returns (status); rpc die (empty) returns (status);
Similar to driver, the following command is executed in the terminal.
$ python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. worker.proto
This generates two files in the directory named, worker_pb2_grpc.py and worker_pb2.py.
Once the proto files are ready, python files for client, driver and worker are written.
The files worker_pb2_grpc.py and worker_pb2.py are imported along with the python libraries. The code for map and reduce are defined in the worker class along with connecting to the driver port.
Mapper function maps input key/value pairs to a set of intermediate key/value pairs. Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs. The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files.
The input text files are opened in read mode. The operations performed on a given input file are converting it to lower case, removing special charectors (other than words) and tranform the document into words. Then each word is sent to a bucket and these are stored in files.
Reducer reduces a set of intermediate values which share a key to a smaller set of values. The numver of reduce operations are drfined in client.py.
In reduce function, glob library is used to extract all files based on a similar id. Then we use the counter function (imported from library collections) to generate a dictionary with the frequency of words.
The worker.py file, all the files generated from proto files and python libraries are imported. The Driver class has several functions. The files are loaded and the worker ports are saved in launchDriver and then connections are established with all the workers. For each worker, map operation is sent along with the parameters. This loop continues untill all the input files are mapped. Then reduce operation is performed similar to the map operation. The driver sents task completed update to the client.
The in-built python libraries are imported along with the files generated from the proto file. A connection channel is established with the driver. Inputs and number of reduce operations and worker ports are initialized when the client is launched. Once the word count operation is performed, it exits with a message.
All the connections communicate through gRPC and evry connection is timed. If a worker or driver connection doesn’t respond within 10 sec, the connection would be timed out. The driver distributed the work based on the information of worker’s state and assigns jobs to the idle workers. If no workers are available, the driver waits and tries again. Once it receives the worker outputs, it aggregates the results.
The input text files are stored in inputs folder. The intermediate files are stored in temp folder and outputs in out folder. The visualization is provided in the video on how the files are created in temp and out during the execution of the code. The video shows the working of the code in visual studio in anaconda environment.
Running the Code
You can run the code with any number of workers and output files. Here, I am running for 3 workers and 6 output files.
- Launch 3 workers by running the following command in the terminal.Provide the port number along with the command.
$ python worker.py 4001
Open another terminal and run the following command:
$ python worker.py 4002
Each worker needs to be declared in a seperate terminal. Open a new terminal and run the following command:
$ python worker.py 4003
- Launch the driver in a new terminal using the following command:
$ python driver.py 4000
- Finally launch the client in a new terminal using the following command:
$ python client.py ./inputs 6 4001 4002 4003
- Bloom RPC can be used to visualize the gRPC server client communication.
- Stagglers can be handled by various methods like replication, etc.
- Divija Swetha Gadiraju [email protected]